Bivariate Fréchet (BF) copulas characterize dependence as a mixture of three simple structures: comonotonicity, independence and countermonotonicity. They are easily interpretable but have limitations when used as approximations to general dependence structures. To improve the approximation property of the BF copulas and keep the advantage of easy interpretation, we develop a new copula approxi...

Monotone patterns of non-response may occur in longitudinal studies. When the measured variables are dependent it is beneficial to use their joint statistical model to impute the missing values. We propose to use vine copulas to factorize the density of the observed variables into a cascade of bivariate copulas that yield a flexible model of their joint distribution. The structure of the vine d...

Applications of copula models have been increasing in number in recent years. This class of models provides a modular parameterization of joint distributions: the specification of the marginal distributions is parameterized separately from the dependence structure of the joint, a convenient way of encoding a model for domains such as finance. Some recent advances on how to specify copulas for a...

We analyze copulas with a non-trivial singular component by using their Markov kernel representation. In particular, we provide existence results for copulas with a prescribed singular component. The constructions do not only help to deal with problems related to multivariate stochastic systems of lifetimes when joint defaults can occur with a non-zero probability, but even provide a copula max...

The copula function is an effective and elegant tool useful for modeling dependence between random variables. Among the many families of this function, one most prominent family Archimedean family, which has its unique structure features. Most functions in have only a single parameter limits scope structure. In paper we modify generator copulas way maintains membership while increasing number p...